Index · Blog
Notes on the work.
Mostly research notes. Pipeline choices, why they matter, what broke first time.
Learning EEG microstates with a variational autoencoder: representation, the explained-variance gap, and the locus of recurrence
A methods note on a Conv-VaDE pipeline for EEG microstates: why topographic images, why the model trails modified k-means on explained variance, the invariance of sequence statistics across deep variants, and the distinction between recurring patterns and recurrent dynamics.
EEG preprocessing, stage by stage
Walking through the seven preprocessing stages that take a raw LEMON recording from disk to the input my Conv-VaDE actually sees.
Four ways to score the same model
Cluster-validity scores disagree on the same model. So do the spaces you evaluate them in. The four-quadrant framework keeps both axes honest.
The four corners of every topomap are not brain signal
Every 40x40 topomap in the pipeline has a constant-zero ring of padded pixels outside the inscribed head-disc. Every loss and metric in the project masks them out. Here is why.